Reverse-Engineering Gene-Regulatory Networks using Evolutionary Algorithms and Grid Computing

Swain, Martin; Hunniford, T.; Dubitzky, Werner; Mandel, Johannes; Palfreyman, Niall

doi:10.1007/s10877-005-0678-x

Cited by 14 publications

(6 citation statements)

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“…It has been used, in fact, to solve NP-complete optimization problems in a wide variety of fields such as chemistry, biology, engineering, astrophysics, aerospace, electronics, mechanical and electrical design, military plans, mathematics, robotics and many others. Notable examples of GAs applications in molecular biology are in modelling of genetic and regulatory networks [99,7,100], predicting protein structure and evolution [101,102], classification of odorant molecules [103], investigation of the metabolome [104]. We have chosen to estimate the unknown parameters of our signalling network model by minimizing the difference between the simulated output of the model and the corresponding experimental observations.…”

Section: Resultsmentioning

confidence: 99%

“…In biology, a classical example of the "inverse" approach is the reconstruction of the three-dimensional structure of macromolecules, using either x-ray diffraction, nuclear magnetic resonance (NMR) or prediction models [4-6]. Another typical biological application of inverse approaches is the reconstruction of gene-regulatory networks [7,8]. …”

Section: Introductionmentioning

confidence: 99%

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Parameter estimate of signal transduction pathways

2006

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BackgroundThe "inverse" problem is related to the determination of unknown causes on the bases of the observation of their effects. This is the opposite of the corresponding "direct" problem, which relates to the prediction of the effects generated by a complete description of some agencies. The solution of an inverse problem entails the construction of a mathematical model and takes the moves from a number of experimental data. In this respect, inverse problems are often ill-conditioned as the amount of experimental conditions available are often insufficient to unambiguously solve the mathematical model. Several approaches to solving inverse problems are possible, both computational and experimental, some of which are mentioned in this article. In this work, we will describe in details the attempt to solve an inverse problem which arose in the study of an intracellular signaling pathway.ResultsUsing the Genetic Algorithm to find the sub-optimal solution to the optimization problem, we have estimated a set of unknown parameters describing a kinetic model of a signaling pathway in the neuronal cell. The model is composed of mass action ordinary differential equations, where the kinetic parameters describe protein-protein interactions, protein synthesis and degradation. The algorithm has been implemented on a parallel platform. Several potential solutions of the problem have been computed, each solution being a set of model parameters. A sub-set of parameters has been selected on the basis on their small coefficient of variation across the ensemble of solutions.ConclusionDespite the lack of sufficiently reliable and homogeneous experimental data, the genetic algorithm approach has allowed to estimate the approximate value of a number of model parameters in a kinetic model of a signaling pathway: these parameters have been assessed to be relevant for the reproduction of the available experimental data.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter estimate of signal transduction pathways

2006

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“…One of the key factors for successful reverse engineering is the selection of an appropriate GRN model. Wahde and Hertz [75] and Jung and Cho [128] have used recurrent NN models; Iba and Mimura [132] and Kimura et al [127] have used S-system models; Repsilber et al [124] have used transition table models; Xiong et al [125] have used linear structural equation models; Swain et al [126] have used mutuality models. Which model is most appropriate to represent the dynamics of GRNs remains unknown.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Reverse engineering of gene regulatory networks

et al. 2007

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Systems biology is a multi-disciplinary approach to the study of the interactions of various cellular mechanisms and cellular components. Owing to the development of new technologies that simultaneously measure the expression of genetic information, systems biological studies involving gene interactions are increasingly prominent. In this regard, reconstructing gene regulatory networks (GRNs) forms the basis for the dynamical analysis of gene interactions and related effects on cellular control pathways. Various approaches of inferring GRNs from gene expression profiles and biological information, including machine learning approaches, have been reviewed, with a brief introduction of DNA microarray experiments as typical tools for measuring levels of messenger ribonucleic acid (mRNA) expression. In particular, the inference methods are classified according to the required input information, and the main idea of each method is elucidated by comparing its advantages and disadvantages with respect to the other methods. In addition, recent developments in this field are introduced and discussions on the challenges and opportunities for future research are provided.

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“…The dynamic activity of GRNs have been revealed by microarray time series experiments that record gene expression [8] and it has been hypothesized that a GRN's structure may be inferred from this, and related, time-series data. This is a reverse-engineering problem in which causes (the GRNs) are deduced from effects (the expression data) [9].…”

Section: Gene Regulatory Networkmentioning

confidence: 99%

Comparing Grid Computing Solutions for Reverse-Engineering Gene Regulatory Networks

Swain

Mandel

Dubitzky

2008

Computational Science – ICCS 2008

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Models of gene regulatory networks encapsulate important features of cell behaviour, and understanding gene regulatory networks is important for a wide range of biomedical applications. Network models may be constructed using reverse-engineering techniques based on evolutionary algorithms. This optimisation process can be very computationally intensive, however its computational requirements can be met using grid computing techniques. In this paper we compare two grid infrastructures. First we implement our reverse-engineering software on an opportunistic grid computing platform. We discuss the advantages and disadvantages of this approach, and then go on to describe an improved implementation using the QosCosGrid, a quasi-opportunistic supercomputing framework (Qos) for complex systems applications (Cos). The QosCosGrid is able to provide advanced support for parallelised applications, across different administrative domains and this allows more sophisticated reverse-engineering approaches to be explored.

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Reverse-Engineering Gene-Regulatory Networks using Evolutionary Algorithms and Grid Computing

Abstract: Determining network models of gene-regulatory networks using evolutionary algorithms not only requires considerable computational power, but also a modeling formalism that can explain the underlying dynamics.

Cited by 14 publications

References 20 publications

Parameter estimate of signal transduction pathways

Parameter estimate of signal transduction pathways

Reverse engineering of gene regulatory networks

Comparing Grid Computing Solutions for Reverse-Engineering Gene Regulatory Networks

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